AirObject: A Temporally Evolving Graph Embedding for Object Identification
This addresses the need for temporally evolving, class-agnostic object representations in robotic tasks such as autonomous exploration, offering a novel approach to a known bottleneck.
The paper tackles the problem of object identification in robotics by proposing AirObject, a temporal 3D object encoding method that builds global keypoint graph-based embeddings from multiple viewpoints, achieving state-of-the-art performance in video object identification with robustness to challenges like occlusion and viewpoint shifts.
Object encoding and identification are vital for robotic tasks such as autonomous exploration, semantic scene understanding, and re-localization. Previous approaches have attempted to either track objects or generate descriptors for object identification. However, such systems are limited to a "fixed" partial object representation from a single viewpoint. In a robot exploration setup, there is a requirement for a temporally "evolving" global object representation built as the robot observes the object from multiple viewpoints. Furthermore, given the vast distribution of unknown novel objects in the real world, the object identification process must be class-agnostic. In this context, we propose a novel temporal 3D object encoding approach, dubbed AirObject, to obtain global keypoint graph-based embeddings of objects. Specifically, the global 3D object embeddings are generated using a temporal convolutional network across structural information of multiple frames obtained from a graph attention-based encoding method. We demonstrate that AirObject achieves the state-of-the-art performance for video object identification and is robust to severe occlusion, perceptual aliasing, viewpoint shift, deformation, and scale transform, outperforming the state-of-the-art single-frame and sequential descriptors. To the best of our knowledge, AirObject is one of the first temporal object encoding methods. Source code is available at https://github.com/Nik-V9/AirObject.